Before taking the self-test, refer back to the learning objectives at the beginning of the module, the notes in the margins, and the glossary at the end of the module.
Use the key at the back of the book to correct your answers.
Restudy pages that correspond to any questions that you answered incorrectly or material you feel uncertain about.
Dynamic programming divides problems into
nodes.
arcs.
decision stages.
branches.
variables.
Possible beginning situations or conditions of a dynamic programming problem are called
stages.
state variables.
decision variables.
optimal policy.
transformation.
The statement concerning the objective of a dynamic programming problem is called
stages.
state variables.
decision variables.
optimal policy.
decision criterion.
The first step of a dynamic programming problem is
to define the nodes.
to define the arcs.
to divide the original problem into stages.
to determine the optimal policy.
none of the above.
In working a problem with dynamic programming, we usually
start at the first part of the problem and work forward to the next parts.
start at the end of the problem and work backward.
start at the most expensive part of the problem.
start at the least expensive part of the problem.
An algebraic statement that reveals the relationship between stages is called
the transformation.
state variables.
decision variables.
the optimal policy.
the decision criterion.
In this module, dynamic programming is used to solve what type of problem?
quantity discount
just-in-time inventory
shortest route
minimal spanning tree
maximal flow
In dynamic programming terminology, a period or logical subproblem is called
the transformation.
a state variable.
a decision variable.
the optimal policy.
a stage.
The statement that the distance from the beginning stage is equal to the distance from the preceding stage to the last node plus the distance from the given stage to the preceding stage is called
the transformation.
state variables.
decision variables.
the optimal policy.
stages.
In dynamic programming, is
the input to the stage n.
the decision at stage n.
the return at stage n.
the output of stage n.
none of the above.
The distance from the beginning stage is equal to the distance from the preceding stage to the last node plus the distance for the given stage to the preceding stage. This relationship is used to solve which type of problem?
knapsack
JIT
shortest route
minimal spanning tree
maximal flow
In using dynamic programming to solve a shortest-route problem, the distance from one point to the next would be called a
state.
stage.
return.
decision.
In using dynamic programming to solve a shortest-route problem, the decision variables at one stage of the problem would be
the distances from one node to the next.
the possible arcs or routes that can be selected.
the number of possible destination nodes.
the entering nodes.
In using dynamic programming to solve a shortest-route problem, the entering nodes would be called
the stages.
the state variables.
the returns.
the decision variables.